class_docstr: A simple neural network for catch.
code: def __call__(self, x: dm_env.TimeStep, state):
      torso_net = hk.Sequential(
          [
              hk.Flatten(),
              hk.Linear(128),
              jax.nn.relu,
              hk.Linear(64),
              jax.nn.relu,
          ]
      )
      torso_output = torso_net(x.observation)
      policy_logits = hk.Linear(self._num_actions)(torso_output)
      value = hk.Linear(1)(torso_output)
      value = jnp.squeeze(value, axis=-1)
      return NetOutput(policy_logits=policy_logits, value=value), state
  
imports: import collections
  import dm_env
  import haiku
  import jax.nn
  import jax.numpy
label_desc: Use image node_id 3 for calling the CatchNet obj's underlying member method code with example usage: obj.__call__(x, state) and returns: NetOutput,  state